---
layout: page
title: Ecosystem
subtitle: Explore a rich ecosystem of libraries, tools, and more to support research and development of Deep Learning application across many fields and domains of application.
action: Get Started
action_url: /get_started
permalink: /ecosystem/

ecosystem_toolkits:
- title: GluonCV
  text: GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.
  icon: /assets/img/visual.svg
  link: https://gluon-cv.mxnet.io
- title: GluonNLP
  text: GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.
  icon: /assets/img/artificial-intelligence.svg
  link: https://gluon-nlp.mxnet.io/
- title: GluonTS
  text: Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
  icon: /assets/img/line-graph.svg
  link: https://gluon-ts.mxnet.io/
- title: AutoGluon
  text: AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data.
  icon: /assets/img/autogluon.png
  link: https://autogluon.mxnet.io

ecosystem_other:
- title: Flower
  text: Flower is an agnostic federated learning framework. Federate any workload, any machine learning framework, and any programming language.
  icon: /assets/img/flower_icon.png
  link: https://flower.dev/
- title: InsightFace
  text: State-of-the-art face detection and face recognition repository, including ArcFace loss and RetinaFace implementation
  link: https://github.com/deepinsight/insightface
- title: Kubeflow
  text: Kubeflow training operator provides Kubernetes custom resources that makes it easy to run distributed or non-distributed model training jobs on Kubernetes for various frameworks, including Apache MXNet.
  icon: /assets/img/kubeflow.png
  link: https://github.com/kubeflow/training-operator
- title: Sockeye
  text: Sockeye is a sequence-to-sequence framework for Neural Machine Translation based on Apache MXNet. It implements state-of-the-art encoder-decoder architectures.
  link: https://awslabs.github.io/sockeye/
- title: Deep Graph Library
  text: DGL is a Python package dedicated to deep learning on graphs supporting MXNet as a backend.
  link: https://www.dgl.ai/
- title: TensorLy
  text: TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.
  icon: /assets/img/tensorly_logo.png
  link: http://tensorly.org/stable/home.html
- title: Apache TVM
  text: Apache TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It supports a number of framework including Apache MXNet.
  link: https://tvm.ai/about
  icon: /assets/img/tvm.png
- title: GluonFR
  text: Community-driven toolkit for Face Recognition and Face Detection
  link: https://gluon-face.readthedocs.io/en/latest/
- title: Optuna
  text: Optuna is a hyperparameter optimization framework that automates the search for good hyperparameters using Python conditionals, loops, and syntax.
  link: https://optuna.org/
  icon: /assets/img/optuna.png
- title: Ray Tune
  text: Tune is a Python library for experiment execution and hyperparameter tuning at any scale.
  link: https://docs.ray.io/en/latest/tune.html
  icon: /assets/img/tune.png
- title: Coach RL
  text: Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms, it supports MXNet as a back-end
  icon: /assets/img/coach_logo.png
  link: https://github.com/NervanaSystems/coach
- title: XFer
  text: Xfer is a library that allows quick and easy transfer of knowledge stored in deep neural networks implemented in Apache MXNet.
  link: https://xfer.readthedocs.io/en/master/
  icon: /assets/img/xfer.png
- title: DJL
  text: Deep Java Library is an open source library to build and deploy deep learning in Java
  icon: /assets/img/djl.png
  link: https://djl.ai/
- title: Multi Model Server
  text: Model Server for Apache MXNet (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
  link: https://github.com/awslabs/multi-model-server
- title: MxNet Sharp
  text: MxNet Sharp package brings efficient and flexible GPU computing and state-of-art deep learning to .NET. It covers all the Imperative, Symbolic and Gluon interface with API's written closer to Python syntax which makes is easier to port python code easily.
  link: https://github.com/deepakkumar1984/MxNet.Sharp
  icon: /assets/img/mxnet_sharp.png
---

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<div class="ecosystem-page">
    <div class="row">
        <h2>D2L.ai</h2>
        <div class="row">
            <div class="col-4">
                <a href="http://d2l.ai/"><img src="{{'/assets/img/front.jpg' | relative_url}}"></a>
            </div>
            <div class="col-8">
                <p>A <a href="https://d2l.ai">deep learning book</a> with interactive jupyter notebooks, math formula,
                    and a dedicated forum for discussions.</p>
                <p>It offers an interactive learning experience with mathematics, figures, code, text, and discussions,
                    where concepts and techniques are illustrated and implemented with experiments on real data
                    sets.</p>
                <p>Each section is an executable Jupyter notebook. You can modify the code and tune hyperparameters to
                    get instant feedback to accumulate practical experiences in deep learning.</p>
                <p>The book is authored by <a href="https://www.astonzhang.com/">Aston Zhang</a>, Amazon Applied
                    Scientist UIUC Ph.D., <a href="http://zacklipton.com/">Zack C. Lipton</a>, CMU Assistant Professor
                    UCSD Ph.D.,
                    <a href="https://scholar.google.com/citations?user=Z_WrhK8AAAAJ&hl=en">Mu Li</a> Amazon Principal
                    Scientist CMU Ph.D. and <a href="https://alex.smola.org/">Alex J. Smola</a> Amazon VP/Distinguished
                    Scientist TU Berlin Ph.D.
                <p>D2L is used as a textbook or a reference book at Carnegie Mellon University, Georgia Institute of
                    Technology, the University of California Berkeley and many more university</p>
            </div>
        </div>
    </div>
    <br><br>
    <h2>Toolkits</h2>
    <div class="row">
        {%- for feature in page.ecosystem_toolkits -%}
        <div class="col-4">
            <div class="card">
                <a href="{{feature.link}}">
                    <div class="card-text">
                        <div class="card-header-title">
                            <h4>{{feature.title}}</h4>
                            <img src="{{feature.icon | relative_url}}">
                        </div>
                        <p class="card-summary">{{feature.text}}</p>
                    </div>
                </a>
            </div>
        </div>
        {%- endfor -%}
    </div>
    <br><br>
    <h2>Ecosystem</h2>
    <div class="row">
        {%- for feature in page.ecosystem_other -%}
        <div class="col-3">
            <div class="card">
                <a href="{{feature.link}}">
                    <div class="card-text">
                        <div class="card-header-title">
                            <h4>{{feature.title}}</h4>
                            <img src="{{feature.icon | relative_url}}">
                        </div>
                        <p class="card-summary">{{feature.text}}</p>
                    </div>
                </a>
            </div>
        </div>
        {%- endfor -%}
    </div>
    <br><br>
</div>
